Overview

Dataset statistics

Number of variables10
Number of observations236010
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 MiB
Average record size in memory73.0 B

Variable types

Boolean1
Numeric9

Alerts

Won is highly correlated with PriceHigh correlation
Price is highly correlated with Won and 1 other fieldsHigh correlation
Coupon is highly correlated with Sensitivity and 1 other fieldsHigh correlation
Notional is highly correlated with MaturityHigh correlation
Maturity is highly correlated with NotionalHigh correlation
Sensitivity is highly correlated with Coupon and 1 other fieldsHigh correlation
Liquidity is highly correlated with Price and 2 other fieldsHigh correlation
Complexity is highly correlated with ParticipantCountHigh correlation
ParticipantCount is highly correlated with ComplexityHigh correlation
Won is highly correlated with PriceHigh correlation
Price is highly correlated with WonHigh correlation
Coupon is highly correlated with Sensitivity and 2 other fieldsHigh correlation
Notional is highly correlated with MaturityHigh correlation
Maturity is highly correlated with NotionalHigh correlation
Sensitivity is highly correlated with Coupon and 1 other fieldsHigh correlation
Liquidity is highly correlated with Coupon and 1 other fieldsHigh correlation
Complexity is highly correlated with ParticipantCountHigh correlation
ParticipantCount is highly correlated with Coupon and 1 other fieldsHigh correlation
Won is highly correlated with PriceHigh correlation
Price is highly correlated with WonHigh correlation
Notional is highly correlated with MaturityHigh correlation
Maturity is highly correlated with NotionalHigh correlation
Sensitivity is highly correlated with LiquidityHigh correlation
Liquidity is highly correlated with SensitivityHigh correlation
Won is highly correlated with PriceHigh correlation
Price is highly correlated with Won and 1 other fieldsHigh correlation
Coupon is highly correlated with Notional and 4 other fieldsHigh correlation
Notional is highly correlated with Coupon and 3 other fieldsHigh correlation
Maturity is highly correlated with Coupon and 1 other fieldsHigh correlation
Sensitivity is highly correlated with Coupon and 3 other fieldsHigh correlation
Liquidity is highly correlated with Price and 3 other fieldsHigh correlation
Complexity is highly correlated with ParticipantCountHigh correlation
ParticipantCount is highly correlated with Coupon and 4 other fieldsHigh correlation
Liquidity has 32072 (13.6%) zeros Zeros

Reproduction

Analysis started2022-03-16 19:22:01.307434
Analysis finished2022-03-16 19:22:21.947854
Duration20.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Won
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size230.6 KiB
False
151002 
True
85008 
ValueCountFrequency (%)
False151002
64.0%
True85008
36.0%
2022-03-16T20:22:21.968743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct236005
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.9922012
Minimum0.09008691816
Maximum638.4642916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-03-16T20:22:22.069397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.09008691816
5-th percentile90.4207775
Q1120.7212158
median141.2023656
Q3174.9049726
95-th percentile306.5616721
Maximum638.4642916
Range638.3742047
Interquartile range (IQR)54.18375684

Descriptive statistics

Standard deviation71.85503254
Coefficient of variation (CV)0.4491158444
Kurtosis8.298806147
Mean159.9922012
Median Absolute Deviation (MAD)25.52098857
Skewness2.533063776
Sum37759759.4
Variance5163.145701
MonotonicityNot monotonic
2022-03-16T20:22:22.205320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.12602344
 
< 0.1%
139.8609082
 
< 0.1%
137.37774092
 
< 0.1%
173.98950641
 
< 0.1%
529.97384441
 
< 0.1%
176.61975121
 
< 0.1%
199.94957111
 
< 0.1%
210.10606621
 
< 0.1%
266.60885621
 
< 0.1%
516.60353491
 
< 0.1%
Other values (235995)235995
> 99.9%
ValueCountFrequency (%)
0.090086918161
< 0.1%
6.7904019341
< 0.1%
12.188176211
< 0.1%
13.512272251
< 0.1%
14.815111031
< 0.1%
14.916944611
< 0.1%
15.88471621
< 0.1%
17.103288411
< 0.1%
17.372203571
< 0.1%
18.184668761
< 0.1%
ValueCountFrequency (%)
638.46429161
< 0.1%
626.18619571
< 0.1%
606.62073581
< 0.1%
603.77080431
< 0.1%
602.99215351
< 0.1%
599.16358251
< 0.1%
594.71931681
< 0.1%
594.09637131
< 0.1%
594.02553621
< 0.1%
593.78379981
< 0.1%

Coupon
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89665
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.096170981
Minimum-83.46062463
Maximum94.24758295
Zeros0
Zeros (%)0.0%
Negative109208
Negative (%)46.3%
Memory size1.8 MiB
2022-03-16T20:22:22.344917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-83.46062463
5-th percentile-30.56478433
Q1-11.19184559
median0.386449391
Q315.28234032
95-th percentile37.86823414
Maximum94.24758295
Range177.7082076
Interquartile range (IQR)26.47418591

Descriptive statistics

Standard deviation20.4824959
Coefficient of variation (CV)9.771386059
Kurtosis0.1403806832
Mean2.096170981
Median Absolute Deviation (MAD)13.10631301
Skewness0.2153492563
Sum494717.3132
Variance419.5326383
MonotonicityNot monotonic
2022-03-16T20:22:22.477163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.24743988812
 
< 0.1%
0.1813379596
 
< 0.1%
-3.9906505116
 
< 0.1%
-10.391742796
 
< 0.1%
48.178172426
 
< 0.1%
34.710706716
 
< 0.1%
-0.1298699356
 
< 0.1%
25.93446756
 
< 0.1%
-21.442014896
 
< 0.1%
-32.372974976
 
< 0.1%
Other values (89655)235944
> 99.9%
ValueCountFrequency (%)
-83.460624632
< 0.1%
-77.736913942
< 0.1%
-75.586696582
< 0.1%
-72.848693732
< 0.1%
-72.691424592
< 0.1%
-72.411465792
< 0.1%
-72.315603612
< 0.1%
-71.814883182
< 0.1%
-70.740593322
< 0.1%
-70.575768562
< 0.1%
ValueCountFrequency (%)
94.247582952
 
< 0.1%
94.154819222
 
< 0.1%
92.48571672
 
< 0.1%
91.910963582
 
< 0.1%
90.191976896
< 0.1%
87.539199082
 
< 0.1%
87.481014842
 
< 0.1%
86.230353542
 
< 0.1%
85.879000966
< 0.1%
83.630882412
 
< 0.1%

Notional
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89664
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5120.182618
Minimum3.141090936
Maximum30923.39528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-03-16T20:22:22.616965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.141090936
5-th percentile72.76703039
Q12523.931183
median4321.443461
Q37014.272504
95-th percentile12472.32768
Maximum30923.39528
Range30920.25419
Interquartile range (IQR)4490.341321

Descriptive statistics

Standard deviation3739.134573
Coefficient of variation (CV)0.7302736742
Kurtosis1.273101675
Mean5120.182618
Median Absolute Deviation (MAD)2109.030974
Skewness1.072787946
Sum1208414300
Variance13981127.36
MonotonicityNot monotonic
2022-03-16T20:22:22.753353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14109093612
 
< 0.1%
8166.0124866
 
< 0.1%
9491.7157736
 
< 0.1%
3128.5845236
 
< 0.1%
15505.102186
 
< 0.1%
10240.322466
 
< 0.1%
17.417324056
 
< 0.1%
9782.3647776
 
< 0.1%
3577.6162456
 
< 0.1%
5101.3286936
 
< 0.1%
Other values (89654)235944
> 99.9%
ValueCountFrequency (%)
3.14109093612
< 0.1%
3.1518812732
 
< 0.1%
3.1959843562
 
< 0.1%
3.2610058462
 
< 0.1%
3.2922079746
< 0.1%
3.3029791876
< 0.1%
3.3147758126
< 0.1%
3.3178542516
< 0.1%
3.344893366
< 0.1%
3.3479440962
 
< 0.1%
ValueCountFrequency (%)
30923.395286
< 0.1%
27373.106152
 
< 0.1%
26926.642172
 
< 0.1%
26524.657572
 
< 0.1%
26448.622022
 
< 0.1%
26019.585312
 
< 0.1%
25460.387826
< 0.1%
25166.794872
 
< 0.1%
25101.075092
 
< 0.1%
24717.998546
< 0.1%

Maturity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89663
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2148.467668
Minimum3.140996773
Maximum9261.68591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-03-16T20:22:22.898193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.140996773
5-th percentile67.91906854
Q11132.880152
median2029.793701
Q33016.114807
95-th percentile4637.971819
Maximum9261.68591
Range9258.544913
Interquartile range (IQR)1883.234655

Descriptive statistics

Standard deviation1384.710505
Coefficient of variation (CV)0.6445107485
Kurtosis0.2645880859
Mean2148.467668
Median Absolute Deviation (MAD)939.606097
Skewness0.5922370313
Sum507059854.4
Variance1917423.183
MonotonicityNot monotonic
2022-03-16T20:22:23.028176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14099677312
 
< 0.1%
46.817068596
 
< 0.1%
1662.8246766
 
< 0.1%
2629.96546
 
< 0.1%
63.654865466
 
< 0.1%
4089.5880976
 
< 0.1%
1831.7699796
 
< 0.1%
1877.3284746
 
< 0.1%
765.35996026
 
< 0.1%
1044.8471466
 
< 0.1%
Other values (89653)235944
> 99.9%
ValueCountFrequency (%)
3.14099677312
< 0.1%
3.1506808632
 
< 0.1%
3.1993575122
 
< 0.1%
3.2684860562
 
< 0.1%
3.2766210016
< 0.1%
3.2968751416
< 0.1%
3.3131106256
< 0.1%
3.3239049816
< 0.1%
3.3266429492
 
< 0.1%
3.3289252666
< 0.1%
ValueCountFrequency (%)
9261.685912
< 0.1%
9232.8341242
< 0.1%
9131.366292
< 0.1%
8853.3506012
< 0.1%
8769.9222742
< 0.1%
8670.7069352
< 0.1%
8631.8303612
< 0.1%
8625.7784812
< 0.1%
8420.4255312
< 0.1%
8273.7965632
< 0.1%

Sensitivity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89665
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.190139414
Minimum-153.9884059
Maximum180.9309226
Zeros0
Zeros (%)0.0%
Negative77064
Negative (%)32.7%
Memory size1.8 MiB
2022-03-16T20:22:23.159459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-153.9884059
5-th percentile-47.06635919
Q1-3.084396446
median5.302198074
Q318.55417332
95-th percentile65.31909165
Maximum180.9309226
Range334.9193285
Interquartile range (IQR)21.63856977

Descriptive statistics

Standard deviation31.48103256
Coefficient of variation (CV)4.378361914
Kurtosis2.193242483
Mean7.190139414
Median Absolute Deviation (MAD)11.00476909
Skewness0.3186246528
Sum1696944.803
Variance991.0554114
MonotonicityNot monotonic
2022-03-16T20:22:23.285567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.26834537312
 
< 0.1%
0.2122722836
 
< 0.1%
12.238237916
 
< 0.1%
-15.669138746
 
< 0.1%
107.7660656
 
< 0.1%
74.486715866
 
< 0.1%
-0.0307767766
 
< 0.1%
48.836375286
 
< 0.1%
15.882385516
 
< 0.1%
29.074436356
 
< 0.1%
Other values (89655)235944
> 99.9%
ValueCountFrequency (%)
-153.98840592
< 0.1%
-137.73861542
< 0.1%
-137.35138532
< 0.1%
-136.69389592
< 0.1%
-136.13458742
< 0.1%
-128.50531972
< 0.1%
-127.7810642
< 0.1%
-127.42175482
< 0.1%
-126.85118082
< 0.1%
-125.56330782
< 0.1%
ValueCountFrequency (%)
180.93092262
 
< 0.1%
177.79462332
 
< 0.1%
174.58319722
 
< 0.1%
173.14642996
< 0.1%
172.97256722
 
< 0.1%
170.95558532
 
< 0.1%
168.76950642
 
< 0.1%
165.91145516
< 0.1%
163.49163312
 
< 0.1%
162.93932956
< 0.1%

Liquidity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct158
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5151730859
Minimum-92
Maximum74
Zeros32072
Zeros (%)13.6%
Negative87296
Negative (%)37.0%
Memory size1.8 MiB
2022-03-16T20:22:23.435381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-92
5-th percentile-26
Q1-5
median0
Q36
95-th percentile25
Maximum74
Range166
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.74567722
Coefficient of variation (CV)28.62276316
Kurtosis2.857320104
Mean0.5151730859
Median Absolute Deviation (MAD)6
Skewness-0.41129248
Sum121586
Variance217.4349968
MonotonicityNot monotonic
2022-03-16T20:22:23.572923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032072
 
13.6%
512012
 
5.1%
111916
 
5.0%
49004
 
3.8%
68642
 
3.7%
28518
 
3.6%
38356
 
3.5%
-18152
 
3.5%
-27272
 
3.1%
-36766
 
2.9%
Other values (148)123300
52.2%
ValueCountFrequency (%)
-926
< 0.1%
-892
 
< 0.1%
-888
< 0.1%
-874
 
< 0.1%
-868
< 0.1%
-854
 
< 0.1%
-8412
< 0.1%
-812
 
< 0.1%
-792
 
< 0.1%
-782
 
< 0.1%
ValueCountFrequency (%)
742
 
< 0.1%
714
 
< 0.1%
682
 
< 0.1%
6710
< 0.1%
664
 
< 0.1%
6514
< 0.1%
6412
< 0.1%
6310
< 0.1%
6216
< 0.1%
6122
< 0.1%

Complexity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89665
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5304514438
Minimum-81.12532545
Maximum104.6758397
Zeros0
Zeros (%)0.0%
Negative122028
Negative (%)51.7%
Memory size1.8 MiB
2022-03-16T20:22:23.707028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-81.12532545
5-th percentile-34.52468058
Q1-14.92465313
median-0.210811993
Q312.90218881
95-th percentile36.10969547
Maximum104.6758397
Range185.8011652
Interquartile range (IQR)27.82684194

Descriptive statistics

Standard deviation21.15746084
Coefficient of variation (CV)-39.8857635
Kurtosis0.0387266155
Mean-0.5304514438
Median Absolute Deviation (MAD)13.95475632
Skewness0.1690620277
Sum-125191.8452
Variance447.638149
MonotonicityNot monotonic
2022-03-16T20:22:23.834702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1861778412
 
< 0.1%
0.0456489766
 
< 0.1%
-7.0360255266
 
< 0.1%
-21.174614966
 
< 0.1%
-43.937021466
 
< 0.1%
-4.5146421786
 
< 0.1%
0.0738286916
 
< 0.1%
-27.091451266
 
< 0.1%
-3.6622636026
 
< 0.1%
-17.929323286
 
< 0.1%
Other values (89655)235944
> 99.9%
ValueCountFrequency (%)
-81.125325456
< 0.1%
-78.63137332
 
< 0.1%
-77.429718912
 
< 0.1%
-76.014534752
 
< 0.1%
-74.879392942
 
< 0.1%
-74.393647892
 
< 0.1%
-73.623826432
 
< 0.1%
-73.541276032
 
< 0.1%
-72.907447472
 
< 0.1%
-72.900023292
 
< 0.1%
ValueCountFrequency (%)
104.67583972
 
< 0.1%
96.639394632
 
< 0.1%
87.354273122
 
< 0.1%
87.238207112
 
< 0.1%
84.640448632
 
< 0.1%
82.916860832
 
< 0.1%
82.230323832
 
< 0.1%
81.61233972
 
< 0.1%
80.323948022
 
< 0.1%
80.110963446
< 0.1%

ParticipantCount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct125
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.47712385
Minimum2
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-03-16T20:22:23.969506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile53
Q168
median72
Q380
95-th percentile94
Maximum135
Range133
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.19338194
Coefficient of variation (CV)0.1659480026
Kurtosis1.359091409
Mean73.47712385
Median Absolute Deviation (MAD)7
Skewness-0.05913523861
Sum17341336
Variance148.6785631
MonotonicityNot monotonic
2022-03-16T20:22:24.103852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7029454
 
12.5%
7111482
 
4.9%
748050
 
3.4%
728044
 
3.4%
737878
 
3.3%
757780
 
3.3%
767644
 
3.2%
777278
 
3.1%
787030
 
3.0%
796456
 
2.7%
Other values (115)134914
57.2%
ValueCountFrequency (%)
22
 
< 0.1%
36
< 0.1%
72
 
< 0.1%
84
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
148
< 0.1%
156
< 0.1%
162
 
< 0.1%
172
 
< 0.1%
ValueCountFrequency (%)
1352
 
< 0.1%
1332
 
< 0.1%
13114
< 0.1%
1304
 
< 0.1%
1292
 
< 0.1%
1282
 
< 0.1%
1276
 
< 0.1%
1266
 
< 0.1%
12412
< 0.1%
12326
< 0.1%

Country
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.52860472
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-03-16T20:22:24.216448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.970333416
Coefficient of variation (CV)0.7792176453
Kurtosis1.800481797
Mean2.52860472
Median Absolute Deviation (MAD)1
Skewness1.620126281
Sum596776
Variance3.88221377
MonotonicityNot monotonic
2022-03-16T20:22:24.301656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
186492
36.6%
280562
34.1%
321416
 
9.1%
410638
 
4.5%
59846
 
4.2%
69344
 
4.0%
78082
 
3.4%
86178
 
2.6%
93452
 
1.5%
ValueCountFrequency (%)
186492
36.6%
280562
34.1%
321416
 
9.1%
410638
 
4.5%
59846
 
4.2%
69344
 
4.0%
78082
 
3.4%
86178
 
2.6%
93452
 
1.5%
ValueCountFrequency (%)
93452
 
1.5%
86178
 
2.6%
78082
 
3.4%
69344
 
4.0%
59846
 
4.2%
410638
 
4.5%
321416
 
9.1%
280562
34.1%
186492
36.6%

Interactions

2022-03-16T20:22:19.815951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:07.549216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.976387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.465606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.977392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.420178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.910246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.479475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.301684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:19.968701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:07.704566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.131492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.632562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.128389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.580424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.081475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.654684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.489255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.127256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:07.867746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.287695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.799165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.286309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.734144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.247863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.832449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.671393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.295087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.030691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.458835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.966871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.441836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.917391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.421815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.997043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.837431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.453566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.175642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.629574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.130561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.592377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.073604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.603273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:17.149590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.994959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.610495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.333815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.803351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.284438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.756234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.233955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.788176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:17.326986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:19.151138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.770309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.497947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:09.981258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.475896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:12.937495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.398868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:15.972814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:17.496836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:19.311359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:20.932409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.653652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.139074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.636321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.092515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.555621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.142555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:17.680161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:19.466191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:21.113119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:08.813934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:10.300550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:11.803856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:13.253797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:14.733326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:16.310597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:18.146152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-16T20:22:19.650145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-16T20:22:24.399246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-16T20:22:24.547835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-16T20:22:24.726848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-16T20:22:24.875341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-16T20:22:21.310945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-16T20:22:21.593879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

WonPriceCouponNotionalMaturitySensitivityLiquidityComplexityParticipantCountCountry
0False173.98950636.6195817190.3037465801.39362312.861949-1130.331649741
1False154.434297-14.4935723907.7546483117.6779596.632008026.388674752
2True140.306546-5.1568481748.4784381390.6540471.066665313.911858733
3False136.18539736.3808862876.6940332325.175004-10.9136004-1.2998651006
4False122.7072731.6970442097.1407651690.1968419.9768765-24.816807693
5True184.95098522.6659739515.4550962535.41452044.538466-8-32.587171627
6False124.534103-13.06086910462.3620802770.476943-26.54668318-9.935466682
7False154.3830210.6711427431.8311811980.909118-2.61328510-30.622396562
8False121.444771-11.0621287645.3926352023.554527-31.97002423-8.898941644
9False129.67893316.9971867194.5891001889.10413131.091381-838.575082877

Last rows

WonPriceCouponNotionalMaturitySensitivityLiquidityComplexityParticipantCountCountry
236000False116.06905941.4807713850.7045543114.106088-4.2976352-0.712307921
236001False62.381892-15.1263398591.0742392261.757349-46.4287163111.851354725
236002False116.19244143.8747833991.5138003227.076297-4.134073-115.610462891
236003False98.45599312.9370473924.7988033172.09372114.4284764-37.476664722
236004False161.135193-8.4853881911.875348516.72114723.490229-28-9.843286768
236005False94.503661-13.3760481722.971234456.919126-19.4911494-8.022165694
236006False140.714386-13.2354994366.4747193488.7751638.640732-331.019047731
236007False128.8144153.9169952709.4581382176.1337274.83135412.553707744
236008False102.134840-22.6297764034.2797163244.09491928.6597343-37.870611511
236009False92.9072656.313884887.411363708.376073-5.8729885-6.891542871